For each cluster of the standard trajectory, calculate the probability of observing a given gene (i.e. the fraction of counts in that cluster from the gene). For genes that are not detected add 1e-07 total counts.

For each cell in the direct programming trajectory, calculate the log-likelihood that it was drawn from each of these clusters. This log-likelihood is from the multinomial distribution function using the probabilities obtained in step 2.

4.Identify and tally the maximum likelihood assignments of all direct programming cells. Normalize raw assignments so that they sum to 100 (giving the percentage). Plot the percentage of direct programming cells assigned to each standard protocol state.

Cell-cycle activity can be estimated from a cell cycle associated gene expression signature; populations that express higher average levels of cell cycle genes are most likely cycling at higher frequency than a population with lower level expression of these genes. In
Figure 4b
and
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we performed an analysis in this spirit to determine which parts of the DP and SP differentiation trajectories appeared to be proliferative, and to estimate where cells are exiting the cell cycle. We computed a proliferation score that was the aggregate expression of a panel of 21 cell cycle related genes: Aurka, Top2a, Ccna2, Ccnd1, Ccnd2, Ccnd3, Ccne1, Ccne2, Ccnb1, Cdk4, Cdk6, Cdk2, Cdk1, Cdkn2b, Cdkn2a, Cdkn2c, Cdkn2d, Cdkn1a, Cdkn1b, Cdkn1c, Mcm6, Cdc20, Plk1, and Pcna. We also computed a cell cycle exit score on the basis of the aggregate expression of a panel of 4 tumor suppressor genes that inhibit the cell cycle: Cdkn1c, Cdkn1b, Cdkn1a, and Cdkn2d. In
Figure 4—figure supplement 1
we show the expression of representative individual genes from this score; in general cell cycle genes were correlated with each other in their expression over cells, as were cell cycle exit genes.

We were interested to convert a bound on the population expression of Olig2, obtained by qPCR, into a bound on the lifetime of a hypothetical rare subpopulation that expressed appreciable levels of this transcript. To proceed, we noted that in SP, Olig2 is expressed 5-fold higher than Gapdh. By contrast, qPCR indicated that in DP, Olig2 was expressed 10-fold lower than Gapdh. Since we took qPCR measurements on every day during differentiation, we next reasoned that the maximum possible number of these intermediate cells would exist if they were spread uniformly across our timecourse (otherwise there would be a spike in their number, increasing the chances of their detection). This is unrealistic, but sets a conservative upper bound. Since the total differentiation protocol took 11 days, any Olig2+intermediate that expressed the gene at the levels seen in SP (five fold higher than Gapdh) must thus exist for less than 11 days/10/5, or less than 0.2 s. To estimate what this lifetime would be for an Olig2+population that expressed just one molecule per cell, we conservatively allowed for Gapdh expression levels of 1000 molecules per cell (smRNA-FISH studies have estimated~200–500 Gapdh copies per cell). The maximum fraction of Olig2+cells expressing one molecule per cell at any timepoint in DP is correspondingly 0.1% (1000/10*100%). Our lifetime calculation becomes 11 days/10, or less than 15 min. Since the timescales of mRNA production and degradation are typically on the order of hours, we therefore concluded that an Olig2+subpopulation must not exist during DP.

How does the transcriptional state of motor neurons produced by both protocols compare to that of motor neurons in vivo? To answer this question we leveraged the ability of single-cell RNA sequencing to compare cell states even within populations that are not pure (also see below for functional comparisons of the phenotypes). We performed three analyses. First, we computed the cosine similarity between the centroids of each cell state in both protocols and primary motor neurons (
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). The specific steps of this analysis were as follows:

Second, we compared single cells from the DP and SP terminal states to pMN reference single cells. The goal of this analysis was to detect whether heterogeneity within the DP and SP terminal states correlated with similarity to pMNs. To gain a broad understanding of the relationship between the DP and SP trajectories to our pMN reference, we initially projected pMN single cells into the PCA-space used in the visualization from
Figure 3A
(
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). The projection was performed by multiplying the pMN counts matrix (after quality filtering cells and z-score normalizing genes) by the PC-loadings matrix that was built on the DP and SP cells as described above. The combined coordinates of all cells in this space were then used as input to SPRING, which builds and visualizes a kNN graph. To generate a more refined visualization of how the terminal populations were related to each other we next constructed a new PC-space on EMN and LMN DP and SP cells, combined with pMNs, following the same steps of SPRING visualization described above (
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). This differs from the original projection in that the second PC-space was constructed to reflect variation within only the mature populations of cells (EMN, LMN and pMN). It thus has higher sensitivity to detect subtle differences between MN subpopulations, because the PC-space does not contain dimensions that describe unrelated process that occur in the process of becoming a neuron from an mES transcriptional state, and thus only introduce noise into the comparison of terminal states. To quantify how similar individual cells were to the pMN reference we performed nearest neighbor analysis on the distance matrix underlying this second SPRING visualization. For each DP or SP terminal state, we asked of every cell whether it had a pMN cell among its 50 most similar cells, and found the fraction of cells with similarity to pMNs. The number of nearest neighbors chosen, in this case 50, sets the size of the region in which we required a pMN cell to fall in order for a cell to be classified as similar to pMN. 50 cells equated to roughly 2% of the total cells in this visualization, and so provided an appropriate and stringent threshold.

Third, we performed differential gene expression analysis, comparing the most mature motor neuron states from each protocol with primary HB9+motor neurons (
Figure 5D
). This analysis was performed as described above.

1.Combine all cell states from both protocols, and from HB9+E13.5 primary tissue, extract PV-genes (as described above), and z-score normalize their expression.

CAC Annual Possum Town Tales

Storytelling Festival

Here is your chance!

Stories and storytelling are at the heart of our human experience.

They keep us tied together as families and communities.

Listening to stories promotes learning and self discovery.

Come explore the power of stories and the places they can transport you.

Thank you to all who attended Festival 2017! We look forward to seeing you at 2018's festival on Friday Saturday, September 28 29!

SHEILA KAY ADAMS

A seventh-generation ballad singer, storyteller, and claw-hammer banjo player, Sheila Kay Adams was born and raised in the Sodom Laurel community of Madison County, North Carolina, an area renowned for its unbroken tradition of unaccompanied singing of traditional southern Appalachian ballads that dates back to the early Scots/Irish and English Settlers in the mid-17th century.

Adams learned to sing from her great-aunt Dellie Chandler Norton and other notable singers in the community such as Dillard Chandler and the Wallin Family (including NEA National Heritage Fellow Doug Wallin). She began performing in public in her teens and, throughout her career she has performed at festivals, events, music camps, and workshops around this country and the United Kingdom.

In 1975, Adams graduated fromMars Hill College. In 2003 she was named Alumna of the Year and later received a LifeWorks recognition in appreciation for her shared commitment to service and responsibility, presented at the college's LifeWorks 150 Alumni Celebration in April 2007.

After teaching in the North Carolina public schools for seventeen years, Adams turned to full-time music and storytelling.

Gene Tagaban, “One Crazy Raven” is an influential storyteller, trainer, speaker, mentor
and
performer.Gene is of the Takdeintaan clan, the Raven, Freshwater Sockeye clan from Hoonah, AK. He is the Child of the Wooshkeetaan clan, the Eagle, Shark clan from Juneau, AK. He is Cherokee, Tlingit
and
Filipino. Gene is a board member and trainer for the Native Wellness Institute. He has been a featured teller at the National Storytelling Festival in Jonesborough, TN, Kansas
City
storytelling Festival, the Bay Area Storytelling festival in Berkley, St. Louis Storytelling Festival and the Singapore International Storytelling Festival. He can be seen on Northwest Indian News and the Native Entertainment Network. He is also featured in the films “Shadow of the Salmon” and Sherman Alexie’s “The Business of Fancydancing.” Gene was honored to perform with the Dalai Lama in the presence of an audience of 16,000 children at the “Seeds of Compassion” gathering in Seattle, WA and the Nature Conservancy's 50th anniversary with Jane Goodall.

Gene’s foremost passion is teaching. Using his gift of storytelling, dance, and music, he travels across the country performing, presenting, and facilitating workshops on prevention, empowerment, leadership, relationship-building, communication, self-awareness, spirit and honor to participants of all ages.